Method and system for using cellular date for transportation planning and engineering

ABSTRACT

Using data from a wireless telephony network to support transportation planning and engineering. Data related to wireless network users is extracted from the wireless network to determine the location of a mobile station. Additional location records for the mobile station can be used to characterize the movement of the mobile station: its speed, its route, its point of origin and destination, and its primary and secondary transportation analysis zones. Aggregating data associated with multiple mobile stations allows characterizing and predicting traffic parameters, including traffic speeds and volumes along routes.

STATEMENT OF RELATED PATENT APPLICATIONS

This non-provisional patent application claims priority under 35 U.S.C.§119 to U.S. Provisional Patent Application No. 60/693,283, titledMethod and System for Using Cellular Data for Transportation Planningand Engineering, filed Jun. 23, 2005. This provisional application ishereby fully incorporated herein by reference.

FIELD OF THE INVENTION

This invention relates to a system and method for using wirelesstelephony network data for transportation planning and engineering. Moreparticularly, this invention relates to determining traffic patterns androad usage based on determining locations over time of wirelesstelephony users to support transportation planning and engineering.

BACKGROUND OF THE INVENTION

Transportation planning and engineering relies heavily on empirical dataand extensive use of data analysis techniques that characterize andpredict the flow of traffic in a geographic region. The use of thesetraffic-related data is not new. Traditional methods of empiricaltransportation data collection include questionnaires/interviews, countstations, speed sensors, video cameras, and other approaches thatprovide information about the movement of people and goods along aspecific transportation corridor or throughout a region.

These traffic-related data, together with additional land-use planningand budget-related data, serve as input parameters for traffic planningand engineering analyses, enabling the identifying of traffic relatedissues and their solutions. These analyses can vary from qualitativeevaluations of traffic characteristics and trends (e.g., that trafficvolume along the I-75 corridor is increasing) to sophisticated modelsthat quantify the traffic flow along multiple routes and predict theeffects of changes to the transportation infrastructure, for example,road closings due to construction, road widening, traffic lightsequencing, or the effect of a new commercial or residentialconstruction project. As with most engineering analyses, the accuracyand usefulness of the results depends, at least in part, on the qualityand quantity of input data. The high cost of data collection using thetraditional methods identified above often requires planners andengineers to make liberal assumptions, extrapolations, and inferencesthat may lead to erroneous conclusions.

Additionally, measurements such as trends in speeds and travel timesthat quantify the effects of and identify the causes of congestion areneeded. These data have traditionally been difficult to capture. In aneffort to relieve traffic congestion, transportation planning andengineering groups spend billions of dollars each year on studies andresearch to help set priorities, define optimum solutions, and conveythese solutions to legislators and the general public.

In view of the foregoing, there is a need for a cost effective systemand method that collects and analyzes traffic data for use in trafficplanning and engineering. The present invention provides a system andmethod that collects and processes information from wireless telephonysystems and users of those systems to support transportation planningand engineering.

SUMMARY OF THE INVENTION

The present invention provides a system and method that collects andprocesses information from wireless telephony systems and users tosupport transportation planning and engineering. In one aspect of theinvention, a system for using data from a wireless telephony network tosupport traffic planning and engineering is disclosed. This systemincludes a data extraction module, logically coupled to one or morewireless telephony networks, operable to receive location dataassociated with a mobile station user of one of the wireless telephonynetworks; and a data analysis module, logically coupled to the dataextraction module, operable to use the location data to supporttransportation planning and engineering.

In another aspect of the present invention, a method for using data froma wireless telephony network to support traffic-planning and engineeringis disclosed. The method includes (1) determining a location of a mobilestation using the wireless telephony network; (2) characterizing atransportation infrastructure within a geographic region; and (3)determining a transportation parameter associated with the mobilestation using the transportation infrastructure. The determinedtransportation parameter supports transportation planning andengineering.

In another aspect of the present invention, a method for using data froma wireless telephony network for associating a mobile station with ageographic region is disclosed. The method includes (1) retrieving alocation record associated with the mobile station; (2) establishing oneor more criteria for associating the mobile station with the primarytransportation analysis zone; and (3) applying the one or more criteriato associate the mobile station with the primary transportation analysiszone. The criteria relate the mobile station to the primarytransportation analysis zone based on a time parameter associated withthe mobile station and the primary transportation analysis zone.

In yet another aspect of the present invention, a method for using datafrom a wireless telephony network for identifying an origin and adestination for a trip made by a user of a mobile station is disclosed.The method includes (1) identifying a first location record for themobile station comprising a first geographic region associated with theorigin of a trip; (2) identifying one or more subsequent locationrecords for the mobile station associated with the trip; and (3)identifying a second geographic region for the destination for the trip.The one or more of the subsequent location records for the mobilestation include the second geographic region for a set time interval.

The aspects of the present invention may be more clearly understood andappreciated from a review of the following detailed description of thedisclosed embodiments and by reference to the drawings and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an operating environment in relation to a wirelesstelephony network in accordance with an exemplary embodiment of thepresent invention.

FIG. 2 presents a block diagram showing components of a transportationplanning and engineering system in accordance with an exemplaryembodiment of the present invention.

FIG. 3 depicts a block diagram of a data extraction module within atransportation planning and engineering system in accordance with anexemplary embodiment of the present invention.

FIG. 4 a presents a block diagram showing components of a transportationplanning and engineering system in accordance with an exemplaryembodiment of the present invention.

FIG. 4 b presents a block diagram showing components of a transportationplanning and engineering system in accordance with an alternativeexemplary embodiment of the present invention.

FIG. 4 c presents a block diagram showing components of a transportationplanning and engineering system in accordance with an alternativeexemplary embodiment of the present invention.

FIG. 4 d presents a block diagram showing components a transportationplanning and engineering system in accordance with an alternativeexemplary embodiment of the present invention.

FIG. 5 depicts a block diagram of a data input and processing modulewithin a transportation planning and engineering system in accordancewith an exemplary embodiment of the present invention.

FIG. 6 depicts a block diagram of a data analysis node within atransportation planning and engineering system in accordance with anexemplary embodiment of the present invention.

FIG. 7 presents a process flow diagram for a Privacy Module inaccordance with an exemplary embodiment of the present invention.

FIG. 8 presents an overall process flow diagram for traffic planning andengineering in accordance with an exemplary embodiment of the presentinvention.

FIG. 9 presents a process flow diagram for generating location recordsin accordance with an exemplary embodiment of the present invention.

FIG. 10 presents a process flow diagram for associating a mobile stationwith a transportation analysis zone in accordance with an exemplaryembodiment of the present invention.

FIG. 11 presents a process flow diagram for identifying a primarytransportation analysis zone for a mobile station in accordance with anexemplary embodiment of the present invention.

FIG. 12 presents a process flow diagram for identifying a secondarytransportation analysis zone for a mobile station in accordance with anexemplary embodiment of the present invention.

FIG. 13 a presents a process flow diagram for generating anorigin-destination matrix in accordance with an exemplary embodiment ofthe present invention.

FIG. 13 b provides a representative example of an origin-destinationmatrix in accordance with an exemplary embodiment of the presentinvention.

FIG. 14 presents a process flow diagram for identifying transportationroutes associated with a transportation analysis zone in accordance withan exemplary embodiment of the present invention.

FIG. 15 presents a process flow diagram for estimating the average speedfor a road segment in accordance with an exemplary embodiment of thepresent invention.

FIG. 16 presents a process flow diagram for estimating traffic volume inaccordance with an exemplary embodiment of the present invention.

FIG. 17 presents a process flow diagram for predicting traffic volume inaccordance with an exemplary embodiment of the present invention.

DETAILED DESCRIPTION OF THE EXEMPLARY EMBODIMENTS

Exemplary embodiments of the present invention provide systems andmethods for using data from a wireless telephony network to supporttransportation planning and engineering. Data related to wirelessnetwork users is extracted from the wireless network to determine thelocation of a mobile station. Additional location records for the mobilestation can be used to characterize the movement of the mobile station:its speed, its route, its point of origin and destination, and itsprimary and secondary transportation analysis zones. Aggregating datafrom multiple mobile stations allows characterizing and predictingtraffic parameters, including traffic speeds and volumes along routes.

FIG. 1 depicts an operating environment in relation to a wirelesstelephony network 100 in accordance with an exemplary embodiment of thepresent invention. Referring to FIG. 1, mobile station (MS) 105transmits signals to and receives signals from a radiofrequencytransmission tower 110 while within a geographic cell covered by thetower. These cells vary in size based on anticipated signal volume. ABase Transceiver System (BTS) 115 is used to provide service to mobilesubscribers within its cell. Several Base Transceiver Systems 115 arecombined and controlled by a Base Station Controller (BSC) 120 through aconnection called the A_(bis) Interface. A Data Extraction Module 160can interface with the A_(bis) Interface line.

A Mobile Switching Center (MSC) 125 does the complex task ofcoordinating all the Base Station Controllers, through the A Interfaceconnection, keeping track of all active mobile subscribers using theVisitor Location Register (VLR) 140, maintaining the home subscriberrecords using the Home Location Register (HLR) 130, and connecting themobile subscribers to the Public Service Telephone Network (PSTN) 145.

The location of a mobile station 105 can be determined by embedding aGPS chip in the mobile station 105, or by measuring certain signalingcharacteristics between the mobile station 105 and the BTS 115. Ineither scenario, the process of locating a mobile station 105 is managedwith a Mobile Positioning System (MPS) 135. The MPS 135 uses the samenetwork resources that are used to manage and process calls, which makesits availability somewhat limited.

The Input Output Gateway (IOG) 150 processes call detail records (CDRs)to facilitate such actions as mobile subscriber billing. The IOG 150receives call-related data from the MSC 125 and can interface with theData Extraction Module 160.

In the exemplary embodiment of the present invention shown in FIG. 3,the Data Extraction Module 160 may receive data from a variety oflocations in the wireless network. These locations include the BSC 120and its interface, through the A_(bis) Interface, with the BTS 115, MSC125, the HLR 130, and the MPS 135. The Data Extraction Module 160 canuse data from any network element that contains at a minimum the mobilestation identifier number, cell ID and a time stamp. Some of the morecommon data sources are discussed below. One of ordinary skill in theart would appreciate that some or all of the functions of the DataExtraction Module 160 could be conducted behind the “firewall” of thewireless telephony network. Alternatively, some or all of the dataextraction operations could be carried out by one or more systemsoutside of the wireless telephony network. For example, a vendor couldoperate a system that extracts information from the IOG 150.

CDRs may be requested from billing distribution centers or thedistribution centers may autonomously send the records via file transferprotocol (FTP). Alternatively the CDRs may be extracted as they areroutinely passed from the IOG 150 to a billing gateway, possiblyutilizing a router that duplicates the packets. The specific method usedwill depend on the equipment and preferences of the wireless serviceprovider.

Handover and Registration messages may be obtained by monitoring theproprietary or standard A-interface signaling between the MSC 125 andthe BSCs 120 that it controls. The Data Extraction Module 160 maymonitor that signaling directly or it may obtain signaling informationfrom a signal monitoring system such as a protocol analyzer. In thelatter case the signaling information may already be filtered to removeextraneous information. See the discussion in conjunction with FIG. 7,below, of the privacy process for an exemplary embodiment of the presentinvention, which removes information that may identify the user of aspecific mobile station 105. Alternatively, these messages may beextracted from a Base Station Manager that continuously monitors messagestreams on the BTS 115.

The inherent nature of cellular technology requires frequent datacommunications between the mobile station 105 and the Wireless TelephonyNetwork 100. The approximate location of the mobile station 105 is oneof the data elements transmitted from the mobile station 105 to thenetwork 100. This “location awareness” is necessary to ensure that callscan be processed without delay or interruption and support enhanced 911initiatives. Other data elements collected by the wireless telephonynetwork 100 include the mobile device identification number and, if acall is involved, the calling or called number.

FIG. 2 presents a block diagram 200 showing components of aTransportation Planning and Engineering System 250 in accordance with anexemplary embodiment of the present invention. Referring to FIGS. 1 and2, the Data Extraction Module 160 is depicted as a component of theWireless Telephony Network 100. One of ordinary skill in the art wouldappreciate that the Data Extraction Module 160 may be operated by awireless network carrier or operated separately from the WirelessTelephony Network 100. In one example, the Data Extraction Module's 160connection with the wireless network 100 would consist of datacommunications links and otherwise operate outside the network. Inanother example, another party (that is, an operator other than thewireless carrier) would operate the Data Extraction Module 160 withinthe Wireless Telephony Network 100.

The Data Extraction Module 160 extracts and manipulates data from theWireless Telephony Network 100. The Data Extraction Module 160 isconnected to a Data Analysis Node 210 such that they can convey data orinstructions to one another. This connection may be any type of dataconnection, such as a local area network, a wide area network, or someother data communications connection. The Data Analysis Node 210operates on the data extracted by the Data Extraction Module 160 tosupport transportation planning and engineering. The Data Analysis Node210 is also connected, again by any type of data connection, to EndUsers 220. These End Users 220 represent the ultimate users of theanalyses generated by the Data Analysis Node 210 and may also supplyparameters used in analyses performed by the Data Analysis Node 210.

The exemplary Data Extraction Module 160 and the Data Analysis Node 220provide two general functions. The Data Extraction Module 160 interfaceswith information sources to receive information from those sources. Thisreceipt of information may be continuous, in the sense that theinformation source supplies information to the Data Extraction Module160 at regular intervals or as available. This receipt may be initiatedby the information source, which may push the information to the DataExtraction Module 160. Other information my be received by the DataExtraction Module 160 based on requests from the Data Extraction Module160 to the information source.

The Data Analysis Node 220 processes the information received by theData Extraction Module 160 to support the needs of the End Users 220.This processing may trigger additional information needs, such that theData Analysis Node 220 requests the information from specificinformation sources through the Data Extraction Module 160.

FIG. 3 depicts a block diagram 300 of a data extraction module within atransportation planning and engineering system in accordance with anexemplary embodiment of the present invention. Referring to FIGS. 1, 2and 3, a Wireless Network Data component 310 is communicated to the DataExtraction Module 160. Specifically, in this exemplary embodiment, theWireless Network Data 310 communicates with a Data Input and ProcessingModule 330. The Data Input and Processing Module 330 and a PrivacyModule 340 are components of a Processor Module 320. The operations ofthe Data Input and Processing Module 330 are discussed in greater detailbelow, in connection with FIG. 5. Similarly, the operations of thePrivacy Module 340 are discussed in greater detail in connection withFIG. 7, below.

The Processor Module 320 connects to a Location Module 350. The LocationModule 350 generates location data associated with mobile stations 105.The Location Module 350 is linked to the Data Analysis Node 210. TheData Analysis Node 210 can access the Location Module 350 to receivelocation information, or other information, associated with one or moremobile stations 105.

The components of the Data Extraction Module 160, can be controlled by aConfiguration and Monitoring Module 360. The Configuration andMonitoring Module 360 monitors the performance of the Data ExtractionModule 160 and sets system operating parameters.

FIG. 4 a presents a block diagram 400 showing components of atransportation planning and engineering system in accordance with anexemplary embodiment of the present invention. Referring to FIG. 4 a,the block diagram 400 depicts a single Data Extraction Module 160 ainteracting with a single Data Analysis Node 210 a.

FIG. 4 b presents a block diagram 410 showing components of atransportation planning and engineering system in accordance with analternative exemplary embodiment of the present invention. Referring toFIG. 4 b, the block diagram 410 depicts multiple Data Extraction Modules160 a, 160 b, 160 c interacting with a single Data Analysis Node 210 a.One of ordinary skill in the art would appreciate that any number ofData Extraction Modules 160 could interact with a single Data AnalysisNode 210. For example, wireless telephony networks for a variety ofwireless carriers could each have a Data Extraction Module 160associated with each individual network. The data extracted by theseData Extraction Modules 160 could all be accessed and operated on by asingle Data Analysis Node 210.

FIG. 4 c presents a block diagram 420 showing components of atransportation planning and engineering system in accordance with analternative exemplary embodiment of the present invention. Referring toFIG. 4 c, the block diagram 420 depicts a single Data Extraction Modules160 a interacting with multiple Data Analysis Nodes 210 a, 210 b, 210 c.One of ordinary skill in the art would appreciate that any number ofData Analysis Nodes 210 could interact with a single Data ExtractionModule 160. For example, individual communities or individual trafficplanning and engineering applications could have a dedicated DataAnalysis Node 210, each linked to a common Data Extraction Module 160.

FIG. 4 d presents a block diagram 430 showing components atransportation planning and engineering system in accordance with analternative exemplary embodiment of the present invention. Referring toFIG. 4 d, the block diagram 430 depicts a multiple Data ExtractionModules 160 a, 160 b, 160 c interacting with multiple Data AnalysisNodes 210 a, 210 b, 210 c. One of ordinary skill in the art wouldappreciate that any number of Data Analysis Nodes 210 could interactwith any number of Data Extraction Module 160. For example, multipleindividual communities or individual traffic planning and engineeringapplications could each have a dedicated Data Analysis Node 210, eachlinked to multiple Data Extraction Module 160, one for each localwireless network carrier.

FIG. 5 depicts a block diagram 500 of a data input and processing modulewithin a transportation planning and engineering system in accordancewith an exemplary embodiment of the present invention. Referring to FIG.5, a Data Input and Processing module 330 exchanges data with a WirelessNetwork Data component 310. A Data Input and Processing Module 330includes file interfaces. These interfaces may be specific for a certainfile type. In the exemplary embodiment depicted in FIG. 5, a Data Inputand Processing Module 330 includes a Flat File Interface 542 and an FTPFile Interface 544. These interfaces can poll the Wireless Network Datacomponent 310, each interface polling the network component thatcontains the specific file type, data files on a local storage drive(flat files) and files at an FTP server (FTP files) in this exemplaryembodiment.

Additionally, the Wireless Network Data component 310 may send acontinuous stream of data to an Other Continuous File Interface 546,i.e., a Data Input and Processing Module 330 does not need to poll thisdata source. These data are taken from a BSC data store 522, MSC and VLRdata store 524, and HLR data store 526 and may include call detailrecords, handover messages, and registration messages. One skilled inthe art would appreciate that a Data Input and Processing Module 330 canbe configured to collect information in whatever form the WirelessNetwork Data 310 generates.

In the exemplary embodiment, a Data Input and Processing Module 330 isalso capable of receiving positioning data from the Wireless NetworkData component 310 that include a mobile positioning system. An MPSInterface 548 interacts directly with an MPS Gateway 528 to request andreceive specific MPS data. Also, the Data Analysis Node 210 can accessdata in concerning cell area coverage from a Cell Sector Coverage Map530.

The file interfaces in a Data Input and Processing Module 330 send thedata to a working directory. Files in the working directory cause eventsto be generated and sent to a Parsing Engine 550 for processing. Themessage contains the file name of the data file to be parsed. From thisname, the most appropriate parser syntax is selected and the file isparsed. The program directory for the exemplary embodiment of thepresent invention contains a parser's subdirectory. Jar files containingparsers are placed in this directory. The name of the jar file mustmatch a class name in the jar file and that class must implement theparser interface. Once implemented, the parser converts the extracteddata into a format that can be used by the Privacy Module 340 andLocation Detection Module 350. When the processing of the file iscomplete, the file is moved to a processed directory. Upon startup ofthe Data Input and Processing Module 330, all the files in the processeddirectory may be purged if they are older than a specified number ofdays. The specific operating parameters, such as how and when to storeand delete data files, is controlled by the Configuration and MonitoringModule 360.

FIG. 6 depicts a block diagram 600 of a data analysis node within atransportation planning and engineering system in accordance with anexemplary embodiment of the present invention. Referring to FIGS. 1, 3,and 6, the Data Analysis Node 210 includes two analysis modules: aGeographic Analyzer 610 and a Traffic Analyzer 620. The GeographicAnalyzer 610 analyzes mobile station 105 location data in associationwith Transportation Analysis Zones (TAZs) and characterizes therelationship of the mobile stations 105 with respect to one or moreTAZs.

Typical transportation planning processes often begin with the step ofdividing an overall study area into sub-regions known as TransportationAnalysis Zones. A typical TAZ is a rectangular area with one principaltype of land use, such as residential dwellings, bounded by segments ofmajor streets. However, a TAZ may vary in size, shape, and land use asrequired to meet a specific planning need. Often, TAZs are smaller andmore numerous in urban areas where traffic is more dense and a finerresolution of traffic patterns is needed for effective traffic planning.

The exemplary Data Analysis Node 210 provides for a flexible way todefine the TAZs to suit a particular purpose. For example, a user maysimply refer to a standard boundary set as defined by a planning agencyor the census bureau or define a completely new set of boundaries. Asuite of geographical information systems (GIS) tools are provided aspart of the Geographic Analyzer 610 that allow the user to create andedit TAZ boundaries. These tools interact with a GIS/SocioeconomicDatabase 640. Additional details on specific analyses performed by theGeographic Analyzer 610 of this exemplary embodiment are presented inconjunction with FIGS. 10-13, below.

Additionally, a Location Database 630 is provided. The Location Database630 stores location records associated with mobile stations 105. TheData Extraction Module 160 generates these location records. Thelocation records may include any of the following information: mobilestation identifier; location of mobile station; time of communicationevent; type of communication event resulting in location record (forexample, call, hand-off, registration, etc.); number called (if a call);mobile station speed; mobile station route; mobile station origin;mobile station destination; mobile station home TAZ; and mobile stationwork TAZ. Some of these information items are discussed below, inconjunction with process flow descriptions regarding the operation ofthe Data Analysis Node 210. The Location Database 630 interacts with theData Extraction Module 160 and the Geographic Analyzer 610 and TrafficAnalyzer 620. In some cases, the results of analyses performed by theGeographic Analyzer 610 and Traffic Analyzer 620 are stored in theLocation Database 630 to support subsequent analyses.

The Traffic Analyzer 620 analyzes traffic flows and patterns as part ofa traffic planning and engineering process. The Traffic Analyzer 620 candetermine traffic routes associated with a given TAZ, estimate the speedof mobile stations 105, and determine the volume of traffic moving onselected traffic routes for a given time. For this latter example, theTraffic Analyzer 620 may report on historical traffic volumes or provideprojections for future traffic volumes based on historical data andplanning assumptions. For example, the Traffic Analyzer 620 may be usedto predict the flow of traffic volumes along specific routes in reactionto a planned change in traffic light sequences or a planned new road.

The Traffic Analyzer 620 interacts with a Transportation Planning andEngineering Database 650. This database includes information concerningtraffic management parameters, such as traffic light sequences and roadvolume limits, and planning scenarios to support “what-if” analyses.Additionally, information in the Transportation Planning and EngineeringDatabase 650 may be used by the Geographic Analyzer 610 to supportdefining TAZs.

Although the diagram 600 depicts the Location Database 630 as part ofthe Data Analysis Node 210, one of ordinary skill in the art wouldappreciate that the Location Database 630 may be a component of the DataExtraction Module 160. The Geographic Analyzer 610 and Traffic Analyzer620 would still interact with the Location Database 630 though anyvariety of data communications means used to interact with a database.

FIG. 7 presents a process flow diagram for a Privacy Module inaccordance with an exemplary embodiment of the present invention.Referring to FIGS. 3 and 7, at step 710, the Privacy Module 340 receivescommunication information. At step 720, the Privacy Module 340 looks upa Communication Unit Identifier associated with the communicationsinformation in a database. This Identifier may be the serial number orphone number of a mobile station. The database includes allCommunication Unit Identifiers processed by the Privacy Module 340. Thisdatabase may be purged periodically, such as when a record is more than24 hours old, to provide an extra measure of privacy. Although thesedata may be regularly purged, any resulting anonymous location recordsmay be maintained for a long time to support ongoing transportationplanning and engineering.

At step 730, the Privacy Module 340 determines if the Communication UnitIdentifier is in the database. If the result of this determination is“NO,” then the Privacy Module 340 creates, at step 740, a uniqueidentifier to map to the Communication Unit Identifier and bothidentifiers are stored in Privacy Module 340 database. This uniqueidentifier could be a serial number, the results of an encryptionalgorithm, or other process for mapping a unique identifier with theCommunication Unit Identifier. If the result of this determination is“YES,” or after step 740 is complete, the Privacy Module 340 retrieves,at step 750, the unique identifier for the communications unit. Thefurther processing of the information uses the unique identifier ratherthan the personal identifying information. The Privacy Module 340 thenmoves to step 760, where it returns to the process that invoked thePrivacy Module 340.

One of ordinary skill in the art would appreciate that the PrivacyModule 340 operations could take place within a Wireless TelephonyNetwork 100 (See FIG. 1) firewall or outside the firewall. Operations ofthe Privacy Module 340 could be conducted by the wireless networkcarrier, a third party vendor, or conducted by the party operating theData Extraction Module 160 or Data Analysis Node 210. Additionally,although a separate Privacy Module 340 database has been discussed, oneof ordinary skill in the art would appreciate that a single databasestructure may be used to support all data storage for the system.

In some cases, the information source may apply it own processes to maskpersonal identifying information. For example, a Wireless TelephonyNetwork 100 may mask personal identifying information prior to conveyingthe information to the Data Extraction Module 160, such as by having asystem that strips this information behind the network's firewall.Alternatively, the data source could contract with a separate dataaggregator that supplies the information to the Data Extraction Module160, after personal identifying information was removed.

FIG. 8 presents an overall process flow diagram 800 for traffic planningand engineering in accordance with an exemplary embodiment of thepresent invention. Referring to FIGS. 1, 2, and 8, at step 810, theTransportation Planning and Engineering System 250 determines thelocation of mobile stations 105. These mobile stations communicatethrough a wireless telephony network, such as Wireless Telephony Network100. In this determination step, the Transportation Planning andEngineering System 250 may collect and store a variety of informationabout the mobile station 250, depending on the amount and accessibilityof the information collected by the wireless network carrier. In thisstep, the Transportation Planning and Engineering System 250 may invokea privacy process, such as the process described above, in connectionwith FIG. 7. Step 810 may be conducted by a wireless network carrier oranother party. Similarly, certain third parties may perform some of thedata collection or location determination processes.

At step 820, the Transportation Planning and Engineering System 250characterizes the transportation infrastructure of a geographic region.This step may include defining TAZs and identifying transportationroutes and the road segments and nodes that make up the routes.Characteristics of one or more wireless telephony networks, such as cellsector coverage, may also be included in this characterization.

At step 830, the Transportation Planning and Engineering System 250determines transportation parameters associated with the geographicregion characterized at step 820. These parameters, such as trafficspeed and traffic volume, are based on mobile station 105 locationdeterminations made at step 810.

At step 840, the Transportation Planning and Engineering System 250supports transportation planning and engineering activities. Thissupport may include providing summary reports of traffic conditions andpredicting the impacts on traffic flow based on planning scenarios.

One of ordinary skill in the art would appreciate that transportationparameters determined using the exemplary process of FIG. 8 can supporta variety of transportation planning and engineering processes. Forexample, the parameters may serve as input to analyses to determinetrends in transportation infrastructure use or the impact of opening anew commercial enterprise, such as a large retail store, in a specificarea. In some cases, the end use of the transportation parameters, thatis, the transportation-related data, may dictate the form and characterof the transportation parameters determined by the process 800.

FIG. 9 presents a process flow diagram 900 for generating locationrecords in accordance with an exemplary embodiment of the presentinvention. Referring to FIGS. 3 and 9, at step 910, the Data ExtractionModule 160 retrieves communications data from the Wireless Network Data310. At step 920, the Data Extraction Module 160 determines if a PrivacyModule, such as Privacy Module 340 should be invoked. If the decision is“YES,” the process 900 initiates a privacy process, such as process 700,discussed above in connection with FIG. 7. If the decision is “NO,” orafter the privacy process has been completed, the process 900 moves tostep 930, where the communication data is characterized. For example,the communication may be a call, a hand-off, or a registration. At step940, the Data Extraction Module 160 generates a location record. At aminimum, this record includes a mobile station identifier, an associatedmobile station location, and a time stamp. The record could haveadditional information, including the nature of the communications, ascharacterized in step 930.

FIG. 10 presents a process flow diagram 1000 for associating a mobilestation with a transportation analysis zone in accordance with anexemplary embodiment of the present invention. Referring to FIGS. 1, 6and 10, at step 1010, one or more Transportation Analysis Zones (TAZs)are established within a geographic region. Typically, the geographicregion represents an area being studied for the purposes oftransportation planning or engineering. Step 1010 can be performedindependently of any of the other steps in this process. That is, thedefinition of TAZs may occur independent from and prior to performingany of the other steps of process 1000.

A TAZ represents a sub-region within a geographic region. A TAZ may bedefined based on land-use boundaries, set geometric parameters, oractual physical or governmental boundaries. How a TAZ is defined mayvary based on the end user of an analysis.

At step 1020, the Geographic Analyzer 610 selects a location record fromthe Location Database 630. At step 1030, the Geographic Analyzer 610identifies a principal TAZ for the mobile station 105 associated withthe location record. A principal TAZ may represent the TAZ where theowner of the mobile station 105 associated with the location recordlives, that is, where the mobile station 105 is located during the timeswhen users are traditionally at “home,” for example from 6:00 pm to 8:00am. Alternatively, the principal TAZ may be where the mobile station 105spends the most time during a day. The details of this step arediscussed in greater detail below, in connection with FIG. 11.

At step 1040, the Geographic Analyzer 610 identifies a secondary TAZ forthe mobile station 105 associated with the location record. Thesecondary TAZ may represent the TAZ where the owner of the mobilestation 105 associated with the location record spends most of atraditional work day, that is, such as from 8:00 am to 6:00 pm onweekdays—a “work” TAZ. The details of this step are discussed in greaterdetail below, in connection with FIG. 12.

At step 1050, the Geographic Analyzer 610 determines if additionallocation records within the Location Database 630 need designations fora primary and a secondary TAZ. If so, the process 1000 returns to step1020. If not, the process ends at step 1060.

One of ordinary skill would appreciate that a mobile station may beassociated with additional TAZs. For example, a secondary (or tertiary)TAZ may represent a commercial retail TAZ, which reflects thetransportation analysis zone where the owner of a mobile stationgenerally shops. This TAZ could be determined based on timing (such asSaturday) and geographic land use of a TAZ (such as a TAZ that includesan area around a shopping mall).

FIG. 11 presents a process flow diagram 1030 for identifying a primarytransportation analysis zone for a mobile station in accordance with anexemplary embodiment of the present invention. Referring to FIGS. 1, 6,and 10, 11, at step 1110, the Geographic Analyzer 610 retrieves alllocation records associated with the mobile station 105 associated withthe location record selected at step 1020. As discussed previously, eachlocation record relates to a specific mobile station 105. At step 1110,each location record associated with a single mobile station 105 isretrieved.

At step 1120, the Geographic Analyzer 610 determines if the retrievedrecords indicate that the mobile station 105 has a primary TAZassociated with it. If the result of this determination is “YES,” theprocess 1030 moves to step 1130 and the Geographic Analyzer 610identifies the characterization of the communication associated with thelocation record selected at step 1020. At step 1140, the GeographicAnalyzer 610 determines if the designation of a primary TAZ isconsistent with the selected location record. For example, the locationrecord may be associated with the initiation of a call from a stationarymobile station within a TAZ that is designated in other location recordsas that mobile station's primary TAZ. In this case, the designationwould be consistent. If the designation is consistent, the process 1030moves to step 1150 and the Geographic Analyzer 610 updates the locationrecord selected at step 1020 to include a primary TAZ.

If the determination at step 1140 is “NO,” then the process 1030 movesto step 1160 and the Geographic Analyzer 610 determines a primary TAZfor the mobile station 105. The primary TAZ may represent the “home” ofthe mobile station user. As such, this determination may be based on thefact, for example, that the last few location records are associatedwith call initiations within the same TAZ (although different from thepreviously designated home TAZ), and all calls initiated after 9:00 pm.One of ordinary skill in the art would appreciate that the GeographicAnalyzer 610 may not be able to identify a primary TAZ, because ofinconsistent location data. In that case, at step 1160, the GeographicAnalyzer 610 would indicate “undetermined” for all of the locationrecords associated with that mobile station 105. The process 1030 moveson to step 1170 all of the location records associated with that mobilestation 105 are updated with the new primary TAZ.

If the determination at step 1120 is “NO,” then the process 1030 movesto step 1180 and the Geographic Analyzer 610 determines if sufficientrecords exist to designate a primary TAZ for a mobile station 105. Ifthe determination at step 1190 is “YES,” then the process 1030 moves tostep 1160 and the Geographic Analyzer 610 determines a primary TAZ forthe mobile station 105. The primary TAZ may represent the “home” of themobile station user. As such, this determination may be based on thefact, for example, that the last few location records are associatedwith call initiations within the same TAZ, and all initiated after 9:00pm. The process 1030 moves on to step 1170 all of the location recordsassociated with that mobile station 105 are updated with the new primaryTAZ.

If the determination at step 1180 is “NO,” then the process 1030 movesto step 1190 and the Geographic Analyzer 610 designates the primary TAZfor all of the location records associated with that mobile station 105as “undetermined.” One of ordinary skill in the art would appreciatethat in many cases, a primary TAZ may never be identified for certainlocation records. These records may correspond to mobile stations 105that pass through the geographic region, such as out-of-state travelerson an interstate highway. One of ordinary skill in the art wouldappreciate that as more location records are collected for a specificmobile station, the system can more likely identify a primary TAZ forthat mobile station.

FIG. 12 presents a process flow diagram 1040 for identifying a secondarytransportation analysis zone for a mobile station in accordance with anexemplary embodiment of the present invention. Referring to FIGS. 1, 6,and 10, 12, at step 1210, the Geographic Analyzer 610 retrieves alllocation records associated with the mobile station 105 associated withthe location record selected at step 1020. As discussed previously, eachlocation record relates to a specific mobile station 105. At step 1210,each location record associated with a single mobile station 105 isretrieved.

At step 1220, the Geographic Analyzer 610 determines if the retrievedrecords indicate that the mobile station 105 has a secondary TAZassociated with it. If the result of this determination is “YES,” theprocess 1040 moves to step 1230 and the Geographic Analyzer 610identifies the characterization of the communication associated with thelocation record selected at step 1020. At step 1240, the GeographicAnalyzer 610 determines if the designation of a secondary TAZ isconsistent with the selected location record. For example, the locationrecord may be associated with the initiation of a call from a stationarymobile station within a TAZ that is designated in other location recordsas that mobile station's secondary TAZ. In this case, the designationwould be consistent. If the designation is consistent, the process 1040moves to step 1250 and the Geographic Analyzer 610 updates the locationrecord selected at step 1020 to include a secondary TAZ.

If the determination at step 1140 is “NO,” then the process 1040 movesto step 1260 and the Geographic Analyzer 610 determines a secondary TAZfor the mobile station 105. The secondary TAZ may represent the “workplace” of the mobile station user. As such, this determination may bebased on the fact, for example, that the last few location records areassociated with call initiations within the same TAZ (although differentfrom the previously designated secondary TAZ), and all initiated around5:00 pm. One of ordinary skill in the art would appreciate that theGeographic Analyzer 610 may not be able to identify a new secondary TAZ,because of inconsistent location data. In that case, at step 1260, theGeographic Analyzer 610 would indicate “undetermined” for all of thelocation records associated with that mobile station 105. The process1040 moves on to step 1270 all of the location records associated withthat mobile station 105 are updated with the new secondary TAZ.

If the determination at step 1220 is “NO,” then the process 1040 movesto step 1280 and the Geographic Analyzer 610 determines if sufficientrecords exist to designate a secondary TAZ for a mobile station 105. Ifthe determination at step 1290 is “YES,” then the process 1040 moves tostep 1260 and the Geographic Analyzer 610 determines a secondary TAZ forthe mobile station 105. The secondary TAZ may represent the “work-place”of the mobile station user. As such, this determination may be based onthe fact, for example, that the last few location records are associatedwith call initiations within the same TAZ, and all initiated around 5:00pm. The process 1040 moves on to step 1270 all of the location recordsassociated with that mobile station 105 are updated with the newsecondary TAZ.

If the determination at step 1280 is “NO,” then the process 1040 movesto step 1290 and the Geographic Analyzer 610 designates the secondaryTAZ for all of the location records associated with that mobile station105 as “undetermined.” As with the case of primary TAZs, one of ordinaryskill in the art would appreciate that in many cases, a secondary TAZwill never be identified for certain location records. These records maycorrespond to mobile stations 105 that pass through the geographicregion, such as out-of-state travelers on an interstate highway. One ofordinary skill in the art would appreciate that as more location recordsare collected for a specific mobile station, the system can more likelyidentify a secondary TAZ for that mobile station.

FIG. 13 a presents a process flow diagram 1300 for generating anorigin-destination (OD) matrix in accordance with an exemplaryembodiment of the present invention. Referring to FIGS. 1, 6, and 13 a,at step 1310, the Geographic Analyzer 610 searches the Location Database630 and identifies location records associated with a trip initiationevent for a mobile station 105 and identifies the TAZ associated withthat location record.

Since the location records for any mobile station do not provide acontinuous picture of the locations of that mobile station, the originor destination of a trip may be determined by observing multiplesequential sightings of the mobile station within the same TAZ over someperiod of time, yet moving within that TAZ. The time of departure isassumed to be the time of the last sighting in the origin TAZ just priorto the change in locations (that is, a move into a new TAZ). A tripinitiation event then is the sequence of location records providing amobile station in the same TAZ over some period of time that alsoindicates that the mobile station is moving.

At step 1320, the Geographic Analyzer 610 identifies location recordsassociated with the mobile station 105 indicating that the mobilestation has moved into an adjacent TAZ. This identification step isrepeated until the event of step 1330. That is, the Geographic Analyzer610 tracks the movement of the mobile station 105 until it determinesthat the mobile station has moved into its destination TAZ. At step1330, the Geographic Analyzer 610 identifies that the mobile station 105has reached a destination TAZ. This determination may be made whenlocation records indicate that, for a certain period of time, thelocation record indicates that the mobile station 105 has remained in aTAZ.

At step 1340, the Geographic Analyzer 610 records a “production” eventfor the TAZ identified at Step 1310 and an “attraction” event for theTAZ identified at step 1330. At step 1350, the Geographic Analyzer 610generates an entry for an OD matrix. Such a matrix can be used toprovide estimates of the movement of traffic throughout a geographicregion. Process 1300 can be used to replace the difficult and oftencostly exercise of using direct measurements and surveys to generate acomparable OD matrix. One of ordinary skill in the art would appreciatethat the estimates generated from the process 1300 may be modified by afactor that accounts for the fact that the estimate is based on cellphone use. For example, the estimates may be adjusted by a factor thatrepresents the ratio of the number of drivers that keep their cell phoneon at all times to the total number of cars.

At step 1360, the Geographic Analyzer 610 determines if location recordsindicate an additional trip initiation event. If the determination is“YES,” the process 1300 returns to step 1310 and repeats. Otherwise, theprocess 1300 moves to step 1370 and ends.

FIG. 13 b provides a representative example of an OD matrix 1380 inaccordance with an exemplary embodiment of the present invention.Referring to FIG. 13 b, the matrix 1380 includes column headings fororigination zones, or TAZs, such as zone “4” 1381. The matrix 1380includes row headings for destination zones, or TAZs, such as zone “1”1382. The matrix 1380 also includes entries, such as entry 1383. Theseentries represent the number of trips that originate in an indicatedorigination zone and terminate in an indicated destination zone. Forexample, the entry 1383 is “123.” This entry 1383 means that 123 tripsoriginated in zone 4 1381 and terminated in zone 1 1382 over a timeperiod of concern. The matrix 1380 measures interzone trips. As such,the entries for a trip originating and ending in the same zone have novalues, such as entry 1384, which is represented by an “x.”

Transportation planners and engineers use the OD matrix in describingtransportation patterns in a region. This matrix has information on thetravel and transportation made between different zones of a region. TheOD matrix provides a simple reference of overall traffic movement andidentifies potential areas of concern, for example, a high-densitydestination area. The OD matrix may be used to identify possible chokepoints in a transportation system. The OD matrix traditionally might beestimated using traffic counts on links in the transport network andother available information. This information on the travel is oftencontained in a target OD matrix. The target OD matrix may be an old(probably outdated) matrix or a result from a sample survey. Resultsfrom the survey must be extrapolated to determine an accurate OD matrix.The present invention provides a more reliable and complete set oftravel observations to produce an accurate picture of the travelpatterns throughout a region.

FIG. 14 presents a process flow diagram 1400 for identifyingtransportation routes associated with a transportation analysis zone inaccordance with an exemplary embodiment of the present invention.Referring to FIGS. 1, 2, 6, and 14, at step 1410, the Traffic Analyzer620 identifies all nodes, road segments, and routes within a TAZ. Nodesare typically located at street intersections, but may also be locatedat points of interest. A segment is the portion of a street joining twonodes. Routes are formed as contiguous sets of road segments withspecific endpoints or end zones. Numbers, or some other type ofdesignator, may be assigned to the nodes, segments, and routes. Thenode, segment, and route designators, along with their attributes, suchas travel times for each segment may be used by the TransportationPlanning and Engineering System 250. During step 1410, the TrafficAnalyzer 620 may access the GIS/Socioeconomic Database 640 to identifynodes, road segments, and routes. Additionally, the Traffic Analyzer 620may access cell sector maps for a wireless telephony network toassociate specific cell sectors with nodes, road segments, and routes.

At step 1420, the Traffic Analyzer 620 assigns a number or otherdesignator to each node, route segment, and route within a TAZ. At step1430, the Traffic Analyzer 620 determines if additional TAZs exist to becharacterized. If the determination is “YES,” the process 1400 returnsto step 1410 and the process is repeated for the next TAZ. If thedetermination at step 1430 is “NO,” the process 1400 moves to step 1440and connection points for adjacent TAZs are identified. In other words,at step 1440, the Traffic Analyzer 620 identifies locations (designatedas nodes) were a road segment crosses the border of adjacent TAZs.

At step 1450, the Traffic Analyzer 620 stores all nodes, road segments,and intra-TAZ and inter-TAZ routes in a database, such as theTransportation Planning and Engineering Database 650. Process 1400 canbe repeated as necessary to update the road information.

FIG. 15 presents a process flow diagram 1500 for estimating the averagespeed for a road segment in accordance with an exemplary embodiment ofthe present invention. Referring to FIGS. 1, 6, and 15, at step 1510,the Traffic Analyzer 620 identifies a mobile station 105 moving along aroute. This step may include the Traffic Analyzer 620 identifying fromthe Location Database 630 location records for a common mobile station105 at times that are close together, where the locations vary. In thiscase, the Traffic Analyzer 620 can determine the road segment or routesthat the mobile station traveled during that time. In some cases, avariety or routes may have been taken between the locations indicated bytwo location records. One of ordinary skill in the art would appreciatethat a number of ways could be used to assign a route, such as shortestdistance, fastest route, or previously traveled route, if historic datafor the mobile station indicates a consistently traveled route.

At step 1520, the Traffic Analyzer 620 estimates the speed of the mobilestation 105 along the road segment or route. This estimate is the traveldistance between two location records divided by the time between thetwo location records. At step 1530, the Traffic Analyzer 620 storesestimated speed value and time interval, that is, the time of day anddate, associated with the mobile station 105 and route. These data maybe stored in the Transportation Planning and Engineering Database 650 orin the Location Database 630. Indeed, one of ordinary skill in the artwould appreciate that a single database could be used to manage all ofthe data associated with the present invention.

At step 1540, the Traffic Analyzer 620 determines if additional mobilestations 105 are moving along the same road segment at the same time, asindicated by location records. If the determination is “YES,” theprocess 1500 returns to step 1510 and repeats the steps for the nextmobile station 105. If the determination is “NO,” the process 1500 movesto step 1550 and the Traffic Analyzer 620 estimates the average speedfor a road segment for each time interval. The average speed for theroad segment may simply be the sum of the speeds for each mobile station105 divided by the number of mobile stations. The speed algorithm mayhave additional levels of sophistication, such as the capability ofscreening out mobile stations 105 that are not associated with cars,such as pedestrians. A time interval is a set time span, such as 7:00 amto 7:10 am on Tuesdays, and the duration of the interval may vary bytime interval. For example, another time interval may be Sunday from 12midnight to 6:00 am.

U.S. Pat. No. 6,842,620, entitled System and Method for ProvidingTraffic Information Using Operational Data of a Wireless Networkdescribes one way that a mobile station's movement can be assigned toroad segments and speed estimated. The specification of U.S. Pat. No.6,842,620 is hereby fully incorporated herein by reference.

At step 1560, the Traffic Analyzer 620 determines if additional roadsegments need to be analyzed. If the determination is “YES,” the process1500 returns to step 1510 and repeats the steps for the next roadsegment. If the determination is “NO,” the process 1500 moves to step1570 and terminates. Process 1500 may be run frequently to updatetransportation route data. Additionally, the process 1500 may be rundaily to establish a complete historical picture of traffic flow in anarea.

FIG. 16 presents a process flow diagram 1600 for estimating trafficvolume in accordance with an exemplary embodiment of the presentinvention. Referring to FIGS. 1, 6, and 16, at step 1610, the TrafficAnalyzer 620 identifies a road segment for a route of interest. At step1620, for a given time interval, the Traffic Analyzer 620 determines thevolume of traffic on a road segment. The time interval may be a specificday and time, such as Mar. 6, 2006 between 7:00 am and 7:10 am or mayrepresent multiple days, such as Tuesday mornings over the past yearbetween 7:00 am and 7:10 am. This volume estimate is based on the numberof mobile stations 105 on the road segment, as indicated in locationrecords. This estimate may be adjusted by a factor to account for thosevehicles that do not have cellular phones on. Also, for an aggregatedtime interval, the volume would typically be reported as a daily averagefor the time interval and may include other statistical measures. Forexample, for the “Tuesday mornings over the past year between 7:00 amand 7:10 am” case, the result may be “47 cars per day on average, plusor minus 7, with a maximum of 68 on Feb. 7, 2006.”

At step 1630, the Traffic Analyzer 620 determines if additional roadsegments comprise the route of interest. If the determination is “YES,”the process 1600 moves to step 1640 and identifies additional roadsegments on the route. This identification may be made by querying theTransportation Planning and Engineering Database 650. The process 1600then moves back to step 1620 and repeats. If the determination is “NO,”the process 1600 moves to step 1650 and determines the volume along theentire route.

At step 1660, the Traffic Analyzer 620 determines if additional timeintervals need to be evaluated. If the determination is “YES,” theprocess 1600 returns to step 1610, using the same route of interest. Ifthe determination is “NO,” the Traffic Analyzer 620 determines ifadditional routes are to be analyzed. If the determination is “YES,” theprocess 1600 returns to step 1610 and identifies a road segment from thenew route of interest. If the determination is “NO,” the process 1600moves to step 1680 and ends.

FIG. 17 presents a process flow diagram 1700 for predicting trafficvolume in accordance with an exemplary embodiment of the presentinvention. Referring to FIGS. 1, 2, 6, and 17, at step 1710, the TrafficAnalyzer 620 identifies a road segment for a route of interest. At step1720, for a given time interval, the Traffic Analyzer 620 determines thehistoric volume of traffic on a road segment. The time interval may be aspecific day and time, such as Mar. 6, 2006 between 7:00 am and 7:10 amor may represent multiple days, such as Tuesday mornings over the pastyear between 7:00 am and 7:10 am. This volume estimate is based on thenumber of mobile stations 105 on the road segment, as indicated inlocation records. This estimate may be adjusted by a factor to accountfor those vehicles that do not have cellular phones on. Also, for anaggregated time interval, the volume would typically be reported as adaily average for the time interval and may include other statisticalmeasures, as discussed above.

At step 1730, the Traffic Analyzer 620 determines if additional roadsegments comprise the route of interest. If the determination is “YES,”the process 1600 moves to step 1740 and identifies additional roadsegments on the route. This identification may be made by querying theTransportation Planning and Engineering Database 650. The process 1700then moves back to step 1720 and repeats. If the determination is “NO,”the process 1700 moves to step 1750 and determines the historic trafficvolume along the entire route.

At step 1760, planning scenario constraints are provided. Theseconstraints may include narrowing a road from two lanes in one direct toone lane (for example, in anticipation of construction activities),changing the sequence of traffic lights at a specific traffic node, orchanging the posted speed along a road segment. These planning scenarioconstraints enable traffic planners to predict the impact of certainchanges to travel route conditions. End users, such as End Users 220,may supply these constraints.

At step 1770, the Traffic Analyzer 620 predicts the volume of traffic onthe route based on the constraints. The prediction may be based onadjusting average speeds along a route and determining the impact onvehicles leaving specific road segments. The results of this type ofanalysis can then be used to modify volume estimates for a road way.

At step 1780, the Traffic Analyzer 620 determines if additionalhistorical data is needed. For example, the Traffic Analyzer 620 mayneed to determine the historical traffic flow along alternate routes todetermine if an increase in traffic congestion along one route may beoffset by more vehicles taking an alternate route.

One of ordinary skill in the art would appreciate that steps 1710through 1750 may occur independently from subsequent steps in theprocess 1700. If the determination is “YES,” the process 1700 returns tostep 1710 and identifies additional routes of interest. If thedetermination is “NO,” the process 1700 moves to step 1790 and ends.

In view of the foregoing, one would appreciate that the presentinvention supports a system and method for using data from a wirelesstelephony network to support transportation planning and engineering.Data related to wireless network users is extracted from the wirelessnetwork to determine the location of a mobile station. Additionallocation records for the mobile station can be used to characterize themovement of the mobile station: its speed, its route, its point oforigin and destination, and its primary and secondary transportationanalysis zones. Aggregating data associated with multiple mobilestations allows characterizing and predicting traffic parameters,including traffic speeds and volumes along routes.

What is claimed is:
 1. A method for providing an origination-destinationmatrix comprising the steps of: identifying a trip initiation event fora trip associated with a mobile station; determining a transportationanalysis zone associated with the trip initiation event; identifying aplurality of location records associated with the trip for the mobilestation, wherein the location records comprise locations of the mobilestation after the trip initiation event; identifying a destination pointassociated with the trip, wherein the destination point comprises one ofthe plurality of location records and the location record represents thefinal location of the mobile station; determining the transportationanalysis zone associated with the destination point; and recording theorigination and destination of the trip in the origination-destinationmatrix, the origination being the transportation analysis zoneassociated with the trip initiation event and destination being thetransportation analysis zone associated with the destination point,wherein the origination-destination matrix comprises at least twodimensions wherein a first dimension comprises a plurality oftransportation analysis zones within a geographic area associated withan origin of a trip and the second dimension comprises a plurality oftransportation analysis zones within a geographic area associated with adestination of a trip.
 2. The method of claim 1 further comprising thestep of estimating the movement of traffic through a geographic regioncomprising the transportation analysis zone associated with the tripinitiation event and the transportation analysis zone associated withthe destination point within a certain period of time.
 3. The method ofclaim 1 further comprising the step of characterizing the purpose of thetrip.
 4. The method of claim 3 wherein the step of characterizing thepurpose of the trip comprises determining whether the trip is to theuser of the mobile station's location of work.
 5. The method of claim 1further comprising the steps of determining if the location associatedwith the trip initiation event is the residence of the user of themobile station and determining if the location associated with the tripinitiation event is the either the workplace of the user of the mobilestation or a commercial retail location visited by the user of themobile station.
 6. The method of claim 1 further comprising the steps ofdetermining a primary transportation analysis zone for the mobilestation.